





import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


_a     :    Any         =						logging.get_logger(__name__)
def 							a_	(    __magic_name__		,     __magic_name__   )				->			Optional[int]:




				"""simple docstring"""






				snake_case   :					List[str]    						=				set()
				snake_case   :					int    						=				[]

				def parse_line(__magic_name__   ):
								for line in fp:
												if isinstance(__magic_name__		,     __magic_name__   ):
																snake_case   :					Optional[int]    						=				line.decode('''UTF-8'''   )
												if "warnings summary (final)" in line:
																continue
												# This means we are outside the body of a warning
												elif not line.startswith(''' '''   ):
																# process a single warning and move it to `selected_warnings`.
																if len(__magic_name__   ) > 0:
																				snake_case   :					Tuple    						=				'''\n'''.join(__magic_name__   )
																				# Only keep the warnings specified in `targets`
																				if any(F": {x}: " in warning for x in targets   ):
																								selected_warnings.add(__magic_name__   )
																				buffer.clear()
																continue
												else:
																snake_case   :					List[Any]    						=				line.strip()
																buffer.append(__magic_name__   )

				if from_gh:
								for filename in os.listdir(__magic_name__   ):
												snake_case   :					List[str]    						=				os.path.join(__magic_name__		,     __magic_name__   )
												if not os.path.isdir(__magic_name__   ):
																# read the file
																if filename != "warnings.txt":
																				continue
																with open(__magic_name__   ) as fp:
																				parse_line(__magic_name__   )
				else:
								try:
												with zipfile.ZipFile(__magic_name__   ) as z:
																for filename in z.namelist():
																				if not os.path.isdir(__magic_name__   ):
																								# read the file
																								if filename != "warnings.txt":
																												continue
																								with z.open(__magic_name__   ) as fp:
																												parse_line(__magic_name__   )
								except Exception:
												logger.warning(
												    F"{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."   )

				return selected_warnings
def 							a_	(    __magic_name__		,     __magic_name__   )				->			Optional[Any]:




				"""simple docstring"""






				snake_case   :					Tuple    						=				set()

				snake_case   :					Tuple    						=				[os.path.join(__magic_name__		,     __magic_name__   ) for p in os.listdir(__magic_name__   ) if (p.endswith('''.zip'''   ) or from_gh)]
				for p in paths:
								selected_warnings.update(extract_warnings_from_single_artifact(__magic_name__		,     __magic_name__   )   )

				return selected_warnings


if __name__ == "__main__":
			def 							a_	(    __magic_name__   )				->			List[Any]:




							"""simple docstring"""






							return values.split(''','''   )

			_a     :    Optional[Any]         =						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
			parser.add_argument(
			    '--output_dir',
			    type=str,
			    required=True,
			    help='Where to store the downloaded artifacts and other result files.',
			)
			parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
			# optional parameters
			parser.add_argument(
			    '--targets',
			    default='DeprecationWarning,UserWarning,FutureWarning',
			    type=list_str,
			    help='Comma-separated list of target warning(s) which we want to extract.',
			)
			parser.add_argument(
			    '--from_gh',
			    action='store_true',
			    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
			)

			_a     :    Any         =						parser.parse_args()

			_a     :    List[str]         =						args.from_gh
			if from_gh:
						# The artifacts have to be downloaded using `actions/download-artifact@v3`
						pass
			else:
						os.makedirs(args.output_dir, exist_ok=True)

						# get download links
						_a     :    List[Any]         =						get_artifacts_links(args.workflow_run_id, token=args.token)
						with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
									json.dump(artifacts, fp, ensure_ascii=False, indent=4)

						# download artifacts
						for idx, (name, url) in enumerate(artifacts.items()):
									print(name)
									print(url)
									print('=' * 80)
									download_artifact(name, url, args.output_dir, args.token)
									# Be gentle to GitHub
									time.sleep(1)

    # extract warnings from artifacts
			_a     :    List[Any]         =						extract_warnings(args.output_dir, args.targets)
			_a     :    Optional[int]         =						sorted(selected_warnings)
			with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
						json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)






'''simple docstring'''







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCAmelCase						):
         return ext

   raise Exception(
       F"""Unable to determine file format from file extension {path}. """
       F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     pipeline(
       task=args.task      ,model=args.model if args.model else None      ,config=args.config      ,tokenizer=args.tokenizer      ,device=args.device      ,)
   A__       =     try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
   A__       =     PipelineDataFormat.from_str(
       format=UpperCAmelCase      ,output_path=args.output      ,input_path=args.input      ,column=args.column if args.column else nlp.default_input_names      ,overwrite=args.overwrite      ,)
   return RunCommand(UpperCAmelCase      ,UpperCAmelCase						)







class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Tuple    ,		a       :  Pipeline    ,		a       :  PipelineDataFormat    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     nlp
    A__       =     reader






 @staticmethod
 def 						_UpperCamelCase (a       :  ArgumentParser    )       ->   str:






    """simple docstring"""


    A__       =     parser.add_parser('run'    ,		help='Run a pipeline through the CLI'    )
    run_parser.add_argument('--task'    ,		choices=get_supported_tasks()    ,		help='Task to run'    )
    run_parser.add_argument('--input'    ,		type=a    ,		help='Path to the file to use for inference'    )
    run_parser.add_argument('--output'    ,		type=a    ,		help='Path to the file that will be used post to write results.'    )
    run_parser.add_argument('--model'    ,		type=a    ,		help='Name or path to the model to instantiate.'    )
    run_parser.add_argument('--config'    ,		type=a    ,		help='Name or path to the model\'s config to instantiate.'    )
    run_parser.add_argument(
        '--tokenizer'    ,		type=a    ,		help='Name of the tokenizer to use. (default: same as the model name)'    )
    run_parser.add_argument(
        '--column'    ,		type=a    ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'    ,		)
    run_parser.add_argument(
        '--format'    ,		type=a    ,		default='infer'    ,		choices=PipelineDataFormat.SUPPORTED_FORMATS    ,		help='Input format to read from'    ,		)
    run_parser.add_argument(
        '--device'    ,		type=a    ,		default=-1    ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'    ,		)
    run_parser.add_argument('--overwrite'    ,		action='store_true'    ,		help='Allow overwriting the output file.'    )
    run_parser.set_defaults(func=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    A__  ,			A__       =     self._nlp, []

    for entry in self._reader:
       A__       =     nlp(**a    ) if self._reader.is_multi_columns else nlp(a    )
       if isinstance(a    ,		a    ):
          outputs.append(a    )
       else:
          outputs += output

        # Saving data
    if self._nlp.binary_output:
       A__       =     self._reader.save_binary(a    )
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""    )
    else:
       self._reader.save(a    )


